


import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 							a						(_SCREAMING_SNAKE_CASE	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				int	    =    DebertaTokenizer
							__UpperCAmelCase       :				str	    =    True
							__UpperCAmelCase       :				Optional[int]	    =    DebertaTokenizerFast

							def        __snake_case   (				self			:    Optional[Any]							)      ->       int:
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														__snake_case						:     Dict									=  [
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''[UNK]''',
														]
														__snake_case						:     Tuple									=  dict(zip(__UpperCAmelCase       ,      range(len(__UpperCAmelCase							)							)							)							)
														__snake_case						:     int									=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														__snake_case						:     Union[str, Any]									=  {'''unk_token''': '''[UNK]'''}

														__snake_case						:     Optional[Any]									=  os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["vocab_file"]							)
														__snake_case						:     Dict									=  os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["merges_file"]							)
														with open(self.vocab_file       ,      "w"       ,      encoding="utf-8"							) as fp:
																					fp.write(json.dumps(__UpperCAmelCase							) + "\n"							)
														with open(self.merges_file       ,      "w"       ,      encoding="utf-8"							) as fp:
																					fp.write("\n".join(__UpperCAmelCase							)							)

							def        __snake_case   (				self			:    str       ,      **lowerCamelCase			:    List[Any]							)      ->       int:
														kwargs.update(self.special_tokens_map							)
														return self.tokenizer_class.from_pretrained(self.tmpdirname       ,      **__UpperCAmelCase							)

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Optional[Any]							)      ->       int:
														__snake_case						:     Tuple									=  '''lower newer'''
														__snake_case						:     str									=  '''lower newer'''
														return input_text, output_text

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       int:
														__snake_case						:     str									=  self.get_tokenizer()
														__snake_case						:     int									=  '''lower newer'''
														__snake_case						:     Tuple									=  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
														__snake_case						:     List[Any]									=  tokenizer.tokenize(__UpperCAmelCase							)
														self.assertListEqual(__UpperCAmelCase       ,      __UpperCAmelCase							)

														__snake_case						:     Union[str, Any]									=  tokens + [tokenizer.unk_token]
														__snake_case						:     Dict									=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)       ,      __UpperCAmelCase							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Dict:
														__snake_case						:     List[str]									=  self.get_tokenizer()
														__snake_case						:     int									=  tokenizer("Hello"       ,      "World"							)
														__snake_case						:     List[str]									=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
														self.assertListEqual(tokd["token_type_ids"]       ,      __UpperCAmelCase							)

							@slow
							def        __snake_case   (				self			:    List[str]							)      ->       List[str]:
														__snake_case						:     Any									=  self.tokenizer_class.from_pretrained("microsoft/deberta-base"							)

														__snake_case						:     Tuple									=  tokenizer.encode("sequence builders"       ,      add_special_tokens=__UpperCAmelCase							)
														__snake_case						:     List[str]									=  tokenizer.encode("multi-sequence build"       ,      add_special_tokens=__UpperCAmelCase							)

														__snake_case						:     Dict									=  tokenizer.encode(
														    "sequence builders"       ,      add_special_tokens=__UpperCAmelCase       ,      add_prefix_space=__UpperCAmelCase							)
														__snake_case						:     List[str]									=  tokenizer.encode(
														    "sequence builders"       ,      "multi-sequence build"       ,      add_special_tokens=__UpperCAmelCase       ,      add_prefix_space=__UpperCAmelCase							)

														__snake_case						:     List[str]									=  tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase							)
														__snake_case						:     Optional[int]									=  tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase       ,      __UpperCAmelCase							)

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode






							@slow
							def        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[int]:
														__snake_case						:     Any									=  [self.tokenizer_class]
														if self.test_rust_tokenizer:
																					tokenizer_classes.append(self.rust_tokenizer_class							)

														for tokenizer_class in tokenizer_classes:
																					__snake_case						:     Dict									=  tokenizer_class.from_pretrained("microsoft/deberta-base"							)

																					__snake_case						:     Dict									=  [
																					    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																					    '''ALBERT incorporates two parameter reduction techniques''',
																					    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																					    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																					    ''' vocabulary embedding.''',
																					]

																					__snake_case						:     Tuple									=  tokenizer(__UpperCAmelCase       ,      padding=__UpperCAmelCase							)
																					__snake_case						:     int									=  [tokenizer.decode(__UpperCAmelCase       ,      skip_special_tokens=__UpperCAmelCase							) for seq in encoding['''input_ids''']]

																					# fmt: off
																					__snake_case						:     Any									=  {
																					    '''input_ids''': [
																					        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
																					    ],
																					    '''token_type_ids''': [
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																					    ],
																					    '''attention_mask''': [
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																					    ]
																					}
																					# fmt: on

																					__snake_case						:     List[str]									=  [
																					    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																					    '''ALBERT incorporates two parameter reduction techniques''',
																					    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																					    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																					    ''' vocabulary embedding.''',
																					]

																					self.assertDictEqual(encoding.data       ,      __UpperCAmelCase							)

																					for expected, decoded in zip(__UpperCAmelCase       ,      __UpperCAmelCase							):
																												self.assertEqual(__UpperCAmelCase       ,      __UpperCAmelCase							)




lowercase			:				Dict   =     [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
														number //= 100000

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowercase			:				list[bool | None]   =     [None] * 10_00_00_00
lowercase			:				int   =     True
lowercase			:				Tuple   =     False






def        snake_case__		(    lowerCamelCase_		):

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							A      :					int      		=					chain(next_number(lowerCamelCase_		)		)
							A      :					Dict      		=					number_chain

							while number < 10000000:
														A      :					Any      		=					number_chain
														number *= 10

							return number_chain






def        snake_case__		(    lowerCamelCase_ = 10000000		):
							for i in range(1     ,				lowerCamelCase_		):
														if CHAINS[i] is None:
																					chain(i + 1		)

							return CHAINS[:number].count(lowerCamelCase_		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"{solution() = }")






